Work

An Improved Grey Wolf Optimization Algorithm with Variable Weights

Year: 2019

Type: article

Abstract: With a hypothesis that the social hierarchy of the grey wolves would be also followed in their searching positions, an improved grey wolf optimization (GWO) algorithm with variable weights (VW-GWO) is proposed. And to reduce the probability of being trapped in local optima, a new governing equation of the controlling parameter is also proposed. Simulation experiments are carried out, and comparisons are made. Results show that the proposed VW-GWO algorithm works better than the standard GWO, the ant lion optimization (ALO), the particle swarm optimization (PSO) algorithm, and the bat algorithm (BA). The novel VW-GWO algorithm is also verified in high-dimensional problems.

Source: Computational Intelligence and Neuroscience

DOI: 10.1155/2019/2981282

Authors: Zheng-Ming Gao, Juan Zhao

Institution: Jingchu University of Technology

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,550

Funders: Hubei Provincial Department of Education, Jingchu University of Technology